Cervical, ovarian, endometrial, vulvar, and vaginal cancers represent a significant health challenge for women globally. Despite medical advancements enabling more accurate diagnoses and timely treatments, the early detection of gynecologic cancers often remains elusive due to the diverse imaging modalities, color variations, and noise factors involved. Additionally, the personalized nature of treating gynecological cancers demands customized therapeutic approaches. The integration of Artificial Intelligence (AI) into clinical practice has the potential to enhance diagnostic accuracy, optimize clinical workflows, and improve patient outcomes significantly. AI's application in gynecological oncology can aid diagnosis and prognosis, offering considerable support in managing malignancies affecting the female reproductive system.

This Research Topic aims to explore AI's role in advancing gynecological oncology, focusing on early cancer detection, cancer risk identification, and personalized treatment strategies to improve patient outcomes. By examining the opportunities and challenges presented by AI-driven advancements, this topic seeks to provide comprehensive insights and foster collaboration among researchers, clinicians, oncologists, radiologists, bioinformaticians, and data scientists.

To gather further insights in AI-assisted gynecologic oncology, we welcome articles addressing, but not limited to, the following themes:

o AI-assisted imaging for gynecologic cancers
o Automated diagnosis of gynecologic cancers
o Approaches to predict patient outcomes and treatment responses
o AI-led genomic and biomarker analysis
o Electronic health record (EHR) applications in clinical decision-making
o AI-based approaches for personalized treatment and precision medicine
o Big data analytics and AI integration in cancer registries and EHR
o AI applications in patient monitoring
o Multimodal approaches for accurate diagnosis

Please note that manuscripts focusing solely on bioinformatics, computational analysis, or public database predictions without independent clinical or biological validation are not suitable for publication in this journal.

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Recent Advancements in AI-Assisted Gynecologic Cancer Detection